Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments.
Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments.
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DOI：
10.1016/j.cels.2016.06.007
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发表时间：
2016-07
期刊：
影响因子：
9.3
通讯作者：
Bar-Joseph Z
中科院分区：
文献类型：
--
作者：
Sefer E;Kleyman M;Bar-Joseph Z
高贯穿时间序列研究的一个重要实验设计问题是准确重建轮廓所需的重复次数。由于预算和样本可用性的限制，更多的重复意味着更少的时间点，反之亦然。我们分析密集和重复采样的性能，通过开发一个理论框架，重点是在很宽的噪声水平的限制，但表达可能的曲线集，并通过分析真实的表达数据。对于理论分析和实验数据，我们观察到，在合理的噪声水平下，时间序列数据中的自相关性允许密集采样，以更好地确定正确的水平，非采样点相比，重复采样。我们的框架的Java实现可以用来确定最好的复制策略，给出预期的噪音。这些结果为大量不使用重复的高通量时间序列实验提供了理论支持。我们的研究表明，当面临预算或样本可用性的限制，研究人员进行时间序列实验应该采样更多的时间点，而不是进行技术重复实验。
An important experimental design question for high throughout time series studies is the number of replicates required for accurate reconstruction of the profiles. Due to budget and sample availability constraints, more replicates imply fewer time points and vice versa. We analyze the performance of dense and replicate sampling by developing a theoretical framework that focuses on a restricted yet expressive set of possible curves over a wide range of noise levels and by analyzing real expression data. For both the theoretical analysis and experimental data we observe that under reasonable noise levels, autocorrelations in the time series data allow dense sampling to better determine the correct levels of non-sampled points when compared to replicate sampling. A Java implementation of our framework can be used to determine the best replicate strategy given the expected noise. These results provide theoretical support to the large number of high throughput time series experiments that do not use replicates. Our study indicates that when facing budget or sample availability constraints researchers performing time series experiments should sample more time points rather than perform technical repeat experiments.